Enhancement of QoS in CloudFront through Optimization of Video Transcoding for Adaptive Streaming (original) (raw)
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International Conference on Automation, Computing and Renewable Systems (ICACRS 2022)-IEEE=XPLORE, 2022
Power and other computing resources can be stored and processed in cloud computing environments. Depending on the features of the clients' devices, video streams, whether they are live or on-demand, often need to be transcoded or converted (such as supported formats, bandwidth, and spatial resolution, for example). Currently, streaming service providers maintain multiple transcoded versions of the same video to serve various client devices because transcoding is a computationally expensive and time-consuming process. To eliminate jitters in accepted streams while transcoding, a task scheduling mechanism is included. To ensure that the consumer receives continuous video content delivery, this technique involves cutting a tiny number of video frames from a video segment. In this evaluation, admission control and scheduling based on QoS-aware video streams is the new task scheduling method for video recording that is suggested. With the help of this framework, streaming platform services have made efficient use of cloud resources while following to the Quality of Service (QoS) standards for video transmissions. The technologies are advancing a scheduling technique that is QoS-aware to effectively map video streams to cloud resources in order to deliver high QoS. The performance of this analysis is calculated on different aspects such as Accuracy, Qos and Recall. In this approach the QoS-aware video streaming based admission control and scheduling for video transcoding in cloud computing will give the best outcomes.
Adaptive QoE-based architecture on cloud mobile media for live streaming
Cluster Computing, 2018
Nowadays, more than 75% of Internet traffic is multimedia traffic, moreover mobile traffic is growing at a rate of 50% each year. All these data together with the evolution of the cloud infrastructures lead us to develop Cloud Mobile Media (CMM) architectures to support the needs demanded by end users. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear in particular for live video streaming applications in order to keep a good Quality of Experience (QoE) of the delivered video. Thus, to keep client's satisfaction within good levels in terms of Mean Opinion Score (MOS), we propose an adaptive QoE-based architecture running on CMM infrastructures for live streaming services. In order to carry out this goal, we propose an estimation of MOS values using an statistical method based on factor analysis. This estimation is based on different measured variables throughout the CMM infrastructure. In addition, we compare the accuracy of the estimated MOS against well-known publicly available video quality algorithms. With these estimations, our proposal is based on two added controllers to the CMM infrastructure: (a) the Software Defined Network controller that acts as a master and (b) the Media Streamer controller. Each one does different actions on the CMM infrastructure in order to maintain and improve the QoE at each end user. Finally, this architecture has been implemented over a fat tree topology in order to show their functionality. The results show that our proposal works properly and it adapts quickly to the network changes in order to deliver a good MOS.
Performability Analysis of an Adaptive-Rate Video-Streaming Service in End-to-End QoS Scenarios
Lecture Notes in Computer Science, 2005
Nowadays, dynamic service management frameworks are proposed to ensure end-to-end QoS. To achieve this goal, it is necessary to manage Service Level Agreements (SLAs), which specify quality parameters of the services operation such as availability and performance. This work is focused on the evaluation of Video-on-Demand (VoD) services in end-to-end QoS scenarios. Based on a straightforward Markov Chain, Markov-Reward Chain (MRC) models are developed in order to obtain various QoS measures of an adaptive VoD service. The MRC model has a clear understanding with the design and operation of the VoD system. In this way, new design options can be proposed and be easily evaluated. To compute performability measures of the MRC model, the randomization method is employed. Predicted model results fit well to the ones taken from a real video-streaming testbed.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-case-study-on-self-adaptive-multimedia-streaming-services-of-cloud-based-mobile-streaming-performance-evaluation https://www.ijert.org/research/a-case-study-on-self-adaptive-multimedia-streaming-services-of-cloud-based-mobile-streaming-performance-evaluation-IJERTV3IS070520.pdf Nowadays Cloud Multimedia Services are very popular because they provide an efficient data processing method and it meets the user demands very effectively. Mobile phones and tabs are very popular with emerging technologies. By using these devices we can get the multimedia information efficiently. Now here considering the limited bandwidth available for mobile streaming and different devices this paper presents the issues of self adaptive multimedia streaming services of cloud based mobile streaming. It mainly focuses on quality of service approach for cloud based mobile streaming. To implement this experiment could provide self adaptive multimedia streaming services for varying devices and network speeds.
The Usage of CDN for Live Video Streaming to Improve QoS. Case Study: 1231 Provider
Journal of Communications, 2020
Streaming is a popular technology used by active users for enjoying audio or video. Broadly, this technology needs high bandwidth to carefully keep its Quality of Service (QoS) at a reasonable level. Without enough bandwidth, a problem arises, such as packet loss. This condition can decrease the quality of content delivery. To properly handle that problem, cache technology can be utilized. One type of these technologies is Content Delivery System (CDN). Naturally, the position of CDN has to be placed not far from the user area, so the access time can be faster than the access time when the CDN is not used. Another contributing factor, such as the right video format selecting can provide a good impact. There are two popular formats for live video streaming, such as HLS (HTTP Live Streaming) and RTMP (Real Time Messaging Protocol). This study is going to elaborate on the comparative between HLS and RTMP with CDN and also without it. The result shows live video streaming with CDN has better performance than without CDN. Index Terms-Live video streaming, content delivery network, packet loss, HTTP live streaming, real time messaging protocol I.
Performance Analysis of Video On-demand and Live Video Streaming using Cloud based Services
Scalable Comput. Pract. Exp., 2020
The advent of Cyber-Physical Systems (CPS) has brought a revolutionary change coined as a mixture of information, communication, computation, and control. With applications in smart grid, health monitoring, automatic avionics, distributed robotics, etc., CPS is currently an area of attention among the academia and industry. The advancement of mobile communications and embedded technology has made it possible to build large scale CPS consisting of the interconnection of mobile phones. These devices collect information about the surrounding environment at any time anywhere basis through real-time video capture. Video streaming has proven to be a massive industry that is growing rapidly playing an important role in everyday life. Customer-driven approach wanting best experience with quality has to be the core offering of contemporary scenario. Video streaming is categorized into Video-On-Demand Streaming (VoDS) and Live Video Streaming (LVS) showing the current state-of-art opportuniti...
CloudMedia: When Cloud on Demand Meets Video on Demand
2011 31st International Conference on Distributed Computing Systems, 2011
Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providin g elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform.
Improving QoS by Enhancing Media Streaming Algorithm in Content Delivery Network
International Journal of Engineering and Advanced Technology, 2019
Media streaming has gained popularity due to convenience of playing it at one’s own leisure. It demands for smooth playing of media. However,with the increasing trend of media streaming and number of online users, it is getting difficult for content providers of popular media contents to handle media playing requests for popular media files. The number of simultaneous requests for media contents may affect uniform delivery of media contents and can lead to lower engagement of end-users.Content Delivery Network (CDN) plays an important role in streaming popular media contents by satisfying end-users’ requests through surrogate servers. However, in order to enhance end-users experience, it is not sufficient to only reduce response time of media segments. It also requires to have lesser number of stalls during media streaming. This entails for redirecting requests to suitable surrogate servers as well as managingthetime duration between delivery of subsequent segments of a media file.T...
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
2021 IEEE 46th Conference on Local Computer Networks (LCN), 2021
Recent studies have revealed that network-assisted techniques, by providing a comprehensive view of the network, improve HTTP Adaptive Streaming (HAS) system performance significantly. This paper leverages the capability of Software-Defined Networking, Network Function Virtualization, and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming (CSDN) framework. We employ virtualized edge entities to collect various information items and run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients' requests by selecting optimal cache servers. In case of a cache miss, a client's request is served by an optimal replacement quality from a cache server, by a quality transcoded from an optimal replacement quality at the edge, or by the originally requested quality from the origin server. Comprehensive experiments conducted on a large-scale testbed demonstrate that CSDN outperforms other approaches in terms of the users' QoE and network utilization.
IJERT-Empirical Analysis of User Based Cloud Mobile Video Streaming
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/empirical-analysis-of-user-based-cloud-mobile-video-streaming https://www.ijert.org/research/empirical-analysis-of-user-based-cloud-mobile-video-streaming-IJERTV3IS20164.pdf A mobile network has unpleasant because of the demands on video traffic; the wireless link capacity cannot keep up with the traffic claim. The break between the traffic claim and the link capability, along with time-varying link conditions, produce the results in poor service quality of video streaming such as long buffering time and irregular disruptions. The cloud computing technology, propose a new mobile video streaming framework of cloud, which has two main parts: adaptive mobile video streaming and efficient social video sharing. It constructs a private agent to provide video streaming services efficiently for each mobile user. Adaptive mobile video streaming lets the private agent adaptively adjust the streaming with a scalable video coding technique based on the feedback of link quality. Efficient social video sharing monitors the social network interactions among mobile users and their private agents try to pref-etch video content in advance. Scalable video coding and adaptive streaming techniques can be jointly combined to accomplish effectively the best possible quality of video streaming services. To implement a prototype of the Cloud framework to demonstrate its concert. It is shown that the private agent in the clouds can effectively provide the adaptive streaming, and carry out video sharing (i.e., pref-etching) based on the social network analysis. Carry out large-scale implementation and with serious consideration on energy and price cost and ignored the cost of encoding workload in the cloud while implementing the prototype.